The present disclosure relates to systems and methods for monitoring a subject. Specifically, the present disclosure relates to systems and methods for directly determining brain states, as well as inferring underlying brain states that would be present in absence of current conditions, by way of analyzing physiological feedback, such as electroencephalogram (“EEG”) data associated with burst suppression states.
Since 1846 and the first public uses of ether as a means to control pain during surgical procedures, anesthesia, analgesics, and other administered compounds to control pain have been a mainstay of medicine. However, while the use of the anesthetic and the number of compounds with anesthetic properties in clinical use have grown astronomically since the initial uses of ether, the scientific understanding of the operation of the body when under anesthesia is still developing. For example, a complete understanding of the effects of anesthesia on patients and operation of the patient's brain over the continuum of “levels” of anesthesia is still lacking. As such, anesthesiologists are trained to recognize the effects of anesthesia and extrapolate an estimate of the “level” of anesthetic influence on a given patient based on the identified effects of the administered anesthesia.
Unfortunately, there are a great number of variables that can influence the effects, effectiveness, and, associated therewith, the “level” of anesthetic influence on a given patient. Some clear variables include physical attributes of the patient, such as age, state of general health, height, or weight, but also less obvious variables that are extrapolated, for example, based on prior experiences of the patient when under anesthesia. When these variables are compounded with the variables of a given anesthesiologists' practices and the variables presented by a particular anesthetic compound or, more so, combination of anesthetic compounds, the proper and effective administration of anesthesia to a given patient can appear to be an art and a science.
The anesthetized brain, though profoundly inactivated, is characterized by rich electrophysiological dynamics. At deep levels of anesthesia, the brain reaches a state of burst suppression. Burst suppression is an electroencephalogram pattern that consists of a quasi-periodic alternation between isoelectric quiescence (suppressions) lasting seconds or minutes as the brain becomes more inactivated, and high-voltage brain activity (bursts). Burst suppression appears to be a fundamental characteristic of the deeply anesthetized brain, and can also occur in a range of conditions including hypothermia, deep general anesthesia, certain infant encephalopathy and coma. It is also used in neurology as an electrophysiological endpoint in pharmacologically induced coma for brain protection after traumatic injury and during status epilepticus. However, despite the presence of burst suppression in this broad range of inactivated brain states, its biophysical mechanisms are poorly understood.
Classically, burst suppression has been regarded as a homogenous brain state. This perspective has been derived from EEG studies that burst and suppressions have been shown to occur concurrently across the scalp. However, because scalp EEG is spatially blurred, the underlying dynamics are not fully understood. In vivo studies in anesthetized animals have helped to identify the potential cellular correlates of burst suppression, showing that although nearly all cortical neurons are inhibited during suppression periods, a subset of thalamocortical neurons can continue firing at delta frequencies.
In search of a more detailed and complete mechanistic understanding, recent studies have shown that burst suppression is associated with enhanced excitability in cortical networks. These studies implicate extracellular calcium as a correlate for the switches between burst and suppression. A recent study has proposed an alternative mechanism, using computational methods, where burst suppression manifests in a state of reduced neuronal activity and cerebral metabolism. In such a state, insufficient production of adenosine triphosphate (“ATP”) in local cortical networks can gate neuronal potassium channels, leading to suppression of action potentials. Such a mechanism accounts for the general features of burst suppression previously observed, as well as its occurrence under multiple etiologies, and also predicts a specific frequency structure for the neuronal activity within each burst.
Thus, as can be appreciated, the underlying phenomenon and, hence, a more encompassing understanding of just one brain state, represented by or correlated with burst suppression, is lacking. As such, the ability to accurately discern the current or predict a future state of the individual based on the observed physiological tracking information, such as elicited by EEG data, has been elusive.
Therefore, it would be desirable to have a system and method to determine or predict a current and/or future state of a subject, based on physiological tracking or monitoring information.